tml-epfl / sharpness-vs-generalization
A modern look at the relationship between sharpness and generalization [ICML 2023]
☆43Updated last year
Related projects ⓘ
Alternatives and complementary repositories for sharpness-vs-generalization
- ☆34Updated 9 months ago
- Towards Understanding Sharpness-Aware Minimization [ICML 2022]☆35Updated 2 years ago
- Distilling Model Failures as Directions in Latent Space☆45Updated last year
- ☆55Updated 4 years ago
- Training vision models with full-batch gradient descent and regularization☆38Updated last year
- ☆17Updated 2 years ago
- Official code for "In Search of Robust Measures of Generalization" (NeurIPS 2020)☆28Updated 3 years ago
- Source code of "What can linearized neural networks actually say about generalization?☆18Updated 3 years ago
- Code relative to "Adversarial robustness against multiple and single $l_p$-threat models via quick fine-tuning of robust classifiers"☆16Updated last year
- ☆25Updated 4 months ago
- Code for the ICLR 2022 paper. Salient Imagenet: How to discover spurious features in deep learning?☆36Updated 2 years ago
- Code for the paper "Evading Black-box Classifiers Without Breaking Eggs" [SaTML 2024]☆19Updated 7 months ago
- Code for the paper "The Journey, Not the Destination: How Data Guides Diffusion Models"☆19Updated 11 months ago
- Spurious Features Everywhere - Large-Scale Detection of Harmful Spurious Features in ImageNet☆29Updated last year
- Sharpness-Aware Minimization Leads to Low-Rank Features [NeurIPS 2023]☆25Updated last year
- An Investigation of Why Overparameterization Exacerbates Spurious Correlations☆30Updated 4 years ago
- Implementation of Confidence-Calibrated Adversarial Training (CCAT).☆45Updated 4 years ago
- ☆13Updated 8 months ago
- Understanding Rare Spurious Correlations in Neural Network☆11Updated 2 years ago
- ☆39Updated 2 years ago
- Simple data balancing baselines for worst-group-accuracy benchmarks.☆40Updated last year
- Code for the paper "A Light Recipe to Train Robust Vision Transformers" [SaTML 2023]☆52Updated last year
- Official repo for the paper "Make Some Noise: Reliable and Efficient Single-Step Adversarial Training" (https://arxiv.org/abs/2202.01181)☆25Updated 2 years ago
- ☆105Updated last year
- Do input gradients highlight discriminative features? [NeurIPS 2021] (https://arxiv.org/abs/2102.12781)☆13Updated last year
- Code for "Just Train Twice: Improving Group Robustness without Training Group Information"☆68Updated 6 months ago
- ☆41Updated last year
- On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them [NeurIPS 2020]☆35Updated 3 years ago
- Dataset Interfaces: Diagnosing Model Failures Using Controllable Counterfactual Generation☆43Updated last year
- SGD with large step sizes learns sparse features [ICML 2023]☆32Updated last year